We also tried to see how well DynamicVit [1]} would perform under different patch keep rates \(\rho \) . Note that, unlike our method, DynamicViT aims to hit a target patch sparsification setting and is not really intended to support a dynamic inference range. Additionally, the \(\rho \)  parameter works a bit differently for DynamicVit as it uses multiple stages of sparsification. Figure REF  shows the dynamic inference performance of DieT-S (224\(\times \) 224 image size) trained under DynamicViT and a cyclic PSS. One interesting observation is that DynamicViT has essentially zero improvement in validation accuracy in the \(\rho =1.0\)  to \(\rho =0.8\)  range compared to the target of \(\rho =0.7\) . We believe this makes sense, as DynamicVit was trained to with additional loss term to support the specific target of \(\rho =0.7\) . By comparison, our cyclic PSS model is trained with a variety of \(\rho \)  settings and therefore achieves a more gradual trade-off between accuracy and the patch keep rate (corresponding to image throughput).
<FIGURE>